The dialectical constitution of mobility and immobility: recovering from the Attabad Landslide disaster, Gojal, Gilgit-Baltistan, Pakistan
Bibliographic record
Abstract
This paper contributes to the critical mobilities literature by analysing local mobilities in Gojal, northern Pakistan in the aftermath of the 2010 Attabad Landslide, in order to develop new insights regarding the dialectical relationship between mobility and immobility. The landslide destroyed a large section of the Karakoram Highway, the region's arterial roadway. Among its disastrous effects was prolonged disruption of the accustomed movements of 20,000 villagers stranded north of the slide. To show how mobility is constituted dialectically in relation to immobility in this context, we detail the social and economic demobilisations Gojalis faced when the highway became impassable, and outline new mobilities they developed to mitigate the disaster of protracted strandedness. Gojalis responded to demobilisation by remobilising, at different scales, along new routes, in different directions and via new mobility platforms, thereby re-establishing circulation as a paradigm of everyday life and demonstrating the paper's argument that disasters are social processes that have simultaneously demobilising and remobilising effects. We conclude that nurturing a multiplicity of mobile relations and practices in several directions and across scales during the disaster recovery process will help Gojalis avoid a similar mobility disaster in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".